Neural coding strategies and mechanisms of competition

Abstract

A long running debate has concerned the question of whether neural
representations are encoded using a distributed or a local coding scheme. In
both schemes individual neurons respond to certain specific patterns of
pre-synaptic activity. Hence, rather than being dichotomous, both coding
schemes are based on the same representational mechanism. We argue that a
population of neurons needs to be capable of learning both local and distributed
representations, as appropriate to the task, and should be capable of generating
both local and distributed codes in response to different stimuli. Many neural
network algorithms, which are often employed as models of cognitive processes,
fail to meet all these requirements. In contrast, we present a neural network
architecture which enables a single algorithm to efficiently learn, and respond
using, both types of coding scheme.